Bird Watching In Lion Country Retail Forex trading explained or BWILC for short, is written by Dirk du Toit aka DrForex. It is an eBook in PDF format that looks. As we will see, trading Forex with options preserves much of the advantages of trading. aka Dr Forex, author of Bird Watching in Lion. Country. One key element of. Option Volatility and Pricing by Natenburg explaining the theory of options. In trading terms, impatience rears its ugly head in major and minor ways, both of which are. Dirk DuToit, in his wonderful e-book “Bird Watching in Lion Country”, distinguishes. Understanding “real leverage” is key to a long investment.

For most professional basketball players, training consists almost exclusively of playing basketball, lifting weights and running. Not so with the Dallas Mavericks Dirk Nowitzki, whose training has included walking on his hands, fencing, rowing and learning to play a musical instrument. Huh?

Okay, some of this makes sense. Fencing helps with footwork and rowing helps build back and shoulder strength for rebounding, but these arent obvious training options if youre aiming to become one of the worlds best professional basketball players. You cant argue with the results--Dirk is an all-star-caliber player, and Dallas has excelled during his tenure.

Long ago at The Beryl Companies, we determined that unconventional training methods might yield the best results for our employees, too. We decided pursuing a standard approach wouldnt help our employees reach the next level, build the skills to successfully serve our customers or give them a desire to stay with our organization over the long term. While most companies say they are devoted to employee development, they dont give employees time to really engage in training. We are committed to sacrificing revenue and over-staffing our team so that personnel have time to participate in training. Ultimately, this commitment is one of the reasons we are more profitable than our publicly traded competitors and why our attrition rate is one-fourth the industry average.

Here are a few unconventional ways to keep your employees growing personally and professionally:

Add a dedicated internal training department.

Having a dedicated internal training department that focuses on employee development around topics such as client interfacing skills, communications skills, software skills and delivering exceptional customer service is an essential element to sustaining a focus on training. Set a goal for every employee to log a specific minimum number of training hours each year; this forces management to monitor employee progress and makes training a priority for employees.

Start a company library and book club.

A true entrepreneur, Walt Disney once said, There is more treasure in books than in all the pirates loot on Treasure Island. Designate a centrally located room to be the company library and stock it with business books and videos. Make it easy for employees to access this library before, during and after work hours. To stimulate interest in the library, consider launching a book club, and even offer to host lunch once each month for employees to gather and discuss what they are reading.

Reimburse employees who purchase any learning or development book if they donate it to the library. However, they must write a one-page summary of what they learned from the book they are donating. Encouraging employees to dig deeper into materials and report highlights to the team increases the likelihood they will absorb and use what they are learning, which is the goal.

Create your own curriculum led by your best employees (regardless of titles).

In addition to an official internal training department, create a development team that includes employees from across the company. Charge them with creating a curriculum that will help employees at all levels enhance their skills. These training forums can be offered through a lunch and learn platform or on Saturdays so that more employees can find time to participate. Some suggested courses: Time Management, Social Styles, Situational Leadership, Effective Business Writing, and Planning for Results. If possible, invite outside inspirational speakers.

For employees with high leadership potential, create a more intense learning track. Think of this as your internal MBA program--perhaps a 10-week curriculum for current leaders and other high-potential employees to help them learn advanced business and relationship skills. Modules might include Managing by Values, Business Acumen, Change Management, and Crucial Conversations. These courses should be led by senior leaders in your company to give the up-and-comers exposure to the war stories that only seasoned employees can share.

Bring rising stars out of the shadows.

Create a formal job shadowing program that will allow team members interested in a new position to shadow an individual in that role for several hours each week. This allows them to learn the job responsibilities and determine if that job is a good fit for their future.

Invest in higher education.

Its not unusual for companies to provide tuition assistance; however, in a tight economy its tempting to trim this benefit. Dont. Furthermore, dont place limitations on reimbursement. Provide assistance to any employee pursuing a degree, no matter the subject.

How will you know if your training program is paying off? Hopefully, youll improve your ability to hire from within, enabling the organization to tap into employees who know your business and culture.

This is the book that started me on the path of trading the Forex. This will be a short review of the 2004 book, Bird Watching in Lion Country. (There is a new 2010 edition of the book out currently.)

- Part 1: How to Build a Bomb

This section explains what a trading system is and who this book is for.

- Part 2: Understanding the Edge

Goes over elements of a proper trading system, what an edge is, and how not to trade.

- Part 3: And All That Jazz

The basics of the Foreign Exchange markets.

- Part 4: Using the Edge

This is the heart and soul of the book.

Using the Edge:

To start off with the book reads great, Dirk takes you through the book from the perspective of some one he is mentoring. The book pulls you in and keeps you intrigued.

This is by no means the "Holy Grail" of trading but what it does hold is a solid trading and money management system that will put you on the path of success.

In Part 4: Using the Edge, Dirk introduces you to his 4x1 trading strategy. The main principle behind the system is to improve your odds on a positive return by incorporating each of the 4 "Edges". He goes into detail on what the 4 Edges are and how when they are brought together they create a profitable trading system. The system itself is not indicator heavy and in fact it's more of a price action system. You let the market decide where it wants to go and you tag along until you reach your profit target and get out.

In other words ". leave a little for the other guy,

. dont be greedy and try to pick tops and bottoms."

Though trading is very individualize, I have taken many of the teachings/insights from this book to heart and have incorporated them in my own trading system.

This is not a get rich quick system but the trading strategy presented in this book is solid and profitable. If you want to learn more or get the book your self, click on the link below to their official page.

forex valuta valutahandel

The forex options market started as an over-the-counter (OTC) financial vehicle for large banks, financial institutions and large international corporations to hedge against foreign currency exposure. Like the forex spot market, the forex options market is considered an interbank market. However, with the plethora of real-time financial data and forex option trading software available to most investors through the internet, todays forex option market now includes an increasingly large number of individuals and corporations who are speculating and/or hedging foreign currency exposure via telephone or online forex trading platforms.

Forex option trading has emerged as an alternative investment vehicle for many traders and investors. As an investment tool, forex option trading provides both large and small investors with greater flexibility when determining the appropriate forex trading and hedging strategies to implement.

Most forex options trading is conducted via telephone as there are only a few forex brokers offering online forex option trading platforms.

Forex Option Defined A forex option is a financial currency contract giving the forex option buyer the right, but not the obligation, to purchase or sell a specific forex spot contract (the underlying) at a specific price (the strike price) on or before a specific date (the expiration date). The amount the forex option buyer pays to the forex option seller for the forex option contract rights is called the forex option premium.

The Forex valuta Option Buyer The buyer, or holder, of a foreign currency option has the choice to either sell the foreign currency option contract prior to expiration, or he or she can choose to hold the foreign currency options contract until expiration and exercise his or her right to take a position in the underlying spot foreign currency. The act of exercising the foreign currency option and taking the subsequent underlying position in the foreign currency spot market is known as assignment or being assigned a spot position.

The only initial financial obligation of the foreign currency option buyer is to pay the premium to the seller up front when the foreign currency option is initially purchased. Once the premium is paid, the foreign currency option holder has no other financial obligation (no margin is required) until the foreign currency option is either offset or expires.

On the expiration date, the call buyer can exercise his or her right to buy the underlying foreign currency spot position at the foreign currency options strike price, and a put holder can exercise his or her right to sell the underlying foreign currency spot position at the foreign currency options strike price. valutahandel Most foreign currency options are not exercised by the buyer, but instead are offset in the market before expiration.

Foreign currency options expires worthless if, at the time the foreign currency option expires, the strike price is out-of-the-money. In simplest terms, a foreign currency option is out-of-the-money if the underlying foreign currency spot price is lower than a foreign currency call options strike price, or the underlying foreign currency spot price is higher than a put options strike price. Once a foreign currency option has expired worthless, the foreign currency option contract itself expires and neither the buyer nor the seller have any further obligation to the other party.

The Forex Option Seller The foreign currency option seller may also be called the writer or grantor of a foreign currency option contract. The seller of a foreign currency option is contractually obligated to take the opposite underlying foreign currency spot position if the buyer exercises his right. In return for the premium paid by the buyer, the seller assumes the risk of taking a possible adverse position at a later point in time in the foreign currency spot market valutakurser .

Initially, the foreign currency option seller collects the premium paid by the foreign currency option buyer (the buyers funds will immediately be transferred into the sellers foreign currency trading account). The foreign currency option seller must have the funds in his or her account to cover the initial margin requirement. If the markets move in a favorable direction for the seller, the seller will not have to post any more funds for his foreign currency options other than the initial margin requirement. However, if the markets move in an unfavorable direction for the foreign currency options seller, the seller may have to post additional funds to his or her foreign currency trading account to keep the balance in the foreign currency trading account above the maintenance margin requirement.

Just like the buyer, the foreign currency option seller has the choice to either offset (buy back) the foreign currency option contract in the options market prior to expiration, or the seller can choose to hold the foreign currency option contract until expiration. If the foreign currency options seller holds the contract until expiration, one of two scenarios will occur: (1) the seller will take the opposite underlying foreign currency spot position if the buyer exercises the option or (2) the seller will simply let the foreign currency option expire worthless (keeping the entire premium) if the strike price is out-of-the-money.

Please note that puts and calls are separate foreign currency options contracts and are NOT the opposite side of the same transaction. For every put buyer there is a put seller, and for every call buyer there is a call seller. The foreign currency options buyer pays a premium to the foreign currency options seller in every option transaction.

Forex Call Option A foreign exchange call option gives the foreign exchange options buyer the right, but not the obligation, to purchase a specific foreign exchange spot contract (the underlying) at a specific price (the strike price) on or before a specific date (the expiration date). The valuta amount the foreign exchange option buyer pays to the foreign exchange option seller for the foreign exchange option contract rights is called the option premium.

Please note that puts and calls are separate foreign exchange options contracts and are NOT the opposite side of the same transaction. For every foreign exchange put buyer there is a foreign exchange put seller, and for every foreign exchange call buyer there is a foreign exchange call seller. The foreign exchange options buyer pays a premium to the foreign exchange options seller in every option transaction.

The Forex Put Option A foreign exchange put option gives the foreign exchange options buyer the right, but not the obligation, to sell a specific foreign exchange spot contract (the underlying) at a specific price (the strike price) on or before a specific date (the expiration date). The amount the foreign exchange option buyer pays to the foreign exchange option seller for the foreign exchange option contract rights is called the option premium.

Please note that puts and calls are separate foreign exchange options contracts and are NOT the opposite side of the same transaction. For every foreign exchange put buyer there is a foreign exchange put seller, and for every foreign exchange call buyer there is a foreign exchange call seller. The foreign exchange options buyer pays a premium to the foreign exchange options seller in every option transaction.

Plain Vanilla Forex Options Plain vanilla options generally refer to standard put and call option contracts traded through an exchange (however, in the case of forex option trading, plain vanilla options would refer to the standard, generic forex option contracts that are traded through an over-the-counter (OTC) forex options dealer or clearinghouse). In simplest terms, vanilla forex options would be defined as the buying or selling of a standard forex call option contract or a forex put option contract.

Exotic Forex Options To understand what makes an exotic forex option exotic, you must first understand what makes a forex option non-vanilla. Plain vanilla forex options have a definitive expiration structure, payout structure and payout amount. Exotic forex option contracts may have a change in one or all of the above features of a vanilla forex option. It is important to note that exotic options, since they are often tailored to a specifics investors needs by an exotic forex options broker, are generally not very liquid, if at all.

Intrinsic Extrinsic Value The price of an FX option is calculated into two separate parts, the intrinsic value and the extrinsic (time) value.

The intrinsic value of an FX option is defined as the difference between the strike price and the underlying FX spot contract rate (American Style Options) or the FX forward rate (European Style Options). The intrinsic value represents the actual value of the FX option if exercised. Please note that the intrinsic value must be zero (0) or above if an FX option has no intrinsic value, then the FX option is simply referred to as having no (or zero) intrinsic value (the intrinsic value is never represented as a negative number). An FX option with no intrinsic value is considered out-of-the-money, an FX option having intrinsic value is considered in-the-money, and an FX option with a strike price at, or very close to, the underlying FX spot rate is considered at-the-money.

The extrinsic value of an FX option is commonly referred to as the time value and is defined as the value of an FX option beyond the intrinsic value. A number of factors contribute to the calculation of the extrinsic value including, but not limited to, the volatility of the two spot currencies involved, the time left until expiration, the riskless interest rate of both currencies, the spot price of both currencies and the strike price of the FX option. It is important to note that the extrinsic value of FX options erodes as its expiration nears. valutahandel An FX option with 60 days left to expiration will be worth more than the same FX option that has only 30 days left to expiration. Because there is more time for the underlying FX rki lan spot price to possibly move in a favorable direction, FX options sellers demand (and FX options buyers are willing to pay) a larger premium for the extra amount of time.

Volatility Volatility is considered the most important factor when pricing forex options and it measures mobilselskaber movements in the price of the underlying. High volatility increases the probability that the forex option could expire in-the-money and increases the risk to the forex option seller who, in turn, can demand a larger premium. An increase in volatility causes an increase in the price of both call and put options.

Delta The delta of a forex option is defined as the change in price of a forex option relative to a change in the underlying forex spot rate. A change in a forex options delta can be influenced by a change in the underlying forex spot rate, a change in volatility, a change in the riskless interest rate of the underlying spot currencies or simply by the passage of time (nearing of the expiration date).

The delta must always be calculated in a range of zero to one (0-1.0). Generally, the delta of a deep out-of-the-money forex option will be closer to zero, the delta of an at-the-money forex option will be near .5 (the probability of exercise is near 50%) and the delta of deep in-the-money forex options will be closer to 1.0. In simplest terms, the closer a forex options strike price is relative to the underlying spot forex rate, the higher the delta because it is more sensitive to a change in the underlying rate. onefone

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Forex Forum mt5 Introduction.

Forex market is high-yield and risky mean of taking profit by operations with the currency rates. Instruments of work at Forex market in many ways determine the result of currency trading made by Forex market participants brokers’ clients. Every Forex broker offers its own terminal, however the most part of brokers and traders concur in choosing MetaTrader 4 and MetaTrader 5 terminals. This forum is created for those who prefer the terminal of MetaTrader series in trading on Forex.

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Forex Forum mt5 dialog with brokers and traders (about brokers).

If you have negative or positive experience of work with Forex broker share it at Forex Forum, related to the questions of Forex service quality. You can leave a comment about your broker telling about advantages or drawbacks of work at Forex with it. The aggregate traders’ reviews of brokers constitute a rating. In this rating you can see the leaders and outsiders of the Forex services market.

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Credit Suisse Group AG investors questioned Tidjane Thiam’s plan to prioritize wealth management over the investment bank and rely on growth in Asia while tapping shareholders to bolster capital.

In the multi-year plan the bank will reorganize along geographical lines and split and shrink the securities unit, Credit Suisse said on Wednesday. The company plans to hold an initial public offering of the Swiss business to raise funds and make acquisitions, while cutting 5,600 jobs across the U. S. the U. K. and Switzerland.

The shares fell after the strategy announcement by Thiam, 53, the chief executive officer recruited in July to rebuild investor confidence in a bank struggling with tougher capital demands and record-low interest rates. The plan didn’t spell out details of how the company will more than double profit in Asia and Thiam said he would not provide a target for profitability because only a “fool” would commit to something he can’t control.

“There’s still a lot of uncertainty over this strategy,” said Lutz Roehmeyer, who helps manage 11 billion euros ($12.8 billion) at LBB Invest in Berlin. “You can’t yet say what the growth is going to look like and where it’s really going to come from. I just don’t know if wealth management really is the panacea.”

The shares dropped as much as 5.2 percent and were 3.9 percent lower as of 4:36 p. m. in Zurich. The STOXX Europe 600 Banks Index fell 0.8 percent.

Profit Falls

Credit Suisse will raise 6.05 billion Swiss francs ($6.3 billion) by selling 1.35 billion francs of stock to select shareholders and 4.7 billion francs of shares to existing investors as regulators in Switzerland prepare to require larger capital buffers. The company “assumes” that Switzerland will raise its leverage ratio to 5 percent, Thiam told reporters. He didn’t say more about who the select investors were.

“We are rebooting the company, we are solving our capital issues,” Thiam, 53, said in an interview with Bloomberg Television. “One of our objectives coming in was to take capital off the table to raise enough capital so that this would not be again a topic of conversation at quarterly results.”

The bank on Wednesday said third-quarter profit fell, missing analyst estimates, in part because of a bigger-than-expected drop in handling clients’ money, the business the company wants to expand. The bank will take a “substantial impairment” charge in the fourth quarter as it writes down goodwill in the investment bank, Chief Financial Officer David Mathers told investors in London.

Credit Suisse aims to sell 20 percent to 30 percent of its Swiss bank in an IPO by 2018, estimating the sale would raise between 2 billion francs and 4 billion francs. The IPO would allow Credit Suisse to buy private banks that will probably come up for sale, Thiam said. The goal is to create a bank focused on wealthy private, corporate and institutional clients, it said.

Credit Suisse aims to return to investors 40 percent of the excess capital expected to reach 23 billion francs to 25 billion francs by 2020. The payout is low and will be seen as disappointing, according Nomura Holdings Inc. analysts led by Jon Peace.

As part of a broad reorganization that includes a management shakeup, Credit Suisse will cut 3.5 billion francs in costs by the end of 2018.

The bank is creating three regionally-focused divisions and dividing the securities unit into a markets business and an investment banking operation to be run by Tim O’Hara and Jim Amine. Credit Suisse named Helman Sitohang and Iqbal Khan among six new board members as Gael de Boissard, Hans-Ulrich Meister and Robert Shafir step down.

Credit Suisse said it will begin by shrinking its capital-intensive macro business down to about a quarter of its size by the end of the year while cutting in half the amount of risk-weighted assets at the prime services unit, which caters to hedge funds.

“The market’s reaction is. ‘Yes, we like what you’re doing, but show us the money,”’ Andrew Parry, head of equities at Hermes Investment Management, told Bloomberg Television. “They’ll have to deliver on that.”

Net income in the third quarter decreased 24 percent to 779 million francs from a year earlier. The average estimate of seven analysts in a Bloomberg survey was for 858 million francs. Net revenue from fixed-income sales and trading plunged 53 percent to 674 million francs as “extreme dislocations” in credit markets resulted in lower client activity, the bank said.

Private banking and wealth management posted 647 million francs in pretax profit, down 31 percent, Credit Suisse said. That missed an average estimate of 896 million francs. Clients trades less and trading and commissions fell, the bank said.

Credit Suisse struck a deal allowing Wells Fargo Co. to hire the Swiss lender’s private-bank employees in the U. S. as the firm retreats from managing wealth in the country.

“Thiam was brought into the bank as CEO to downsize the investment bank, grow in Asia and better control costs,” Dirk Becker, an analyst at Kepler Cheuvreux said in a note. “He is delivering on all these expectations with the strategy announcement, but there is no tangible breakthrough on top of it.”

Forex books: We acknowledge the classics and pinpoint the best of the neglected sleepers…

Dont neglect the classics, market fundamentals persist over time

Anyone tasked with the job of compiling a 10 Best list of anything is immediately faced with limitations. There are obviously more than 10 good forex books. And any one list of the 10 best forex books is bound to disappoint some people with its inclusions and others by its exclusions. So this is not intended to be the definitive list.

Do you have a book youd like to recommend? Better yet, what is your list of the top ten forex books? Leave a comment below and tell us.

These are the top 10 forex books for trading that Ive found in my experience: (1) Trading in the Zone, by Mark Douglas Often referred to as the Bible Of Trading, and as applicable to the forex markets as any market. Simply the most important book you will ever read as a trader, and you will read it often, I promise. The first time I read it I had just started trading and didnt get much out of it. A year later I read it again and it was like the clouds parting and the sun shining through! An absolute must readTrading in the Zone: Master the Market with Confidence, Discipline and a Winning Attitude

Yes I know its two books, but seriously anything you can lay your hands on to do with Jesse Livermore is well worth reading, and forget about the fact that he was mostly a stock trader, the lessons and insights gained here apply to any market and any trading activity, and are quite timeless. Jesse Livermore: Worlds Greatest Stock Trader

Reminiscences of a Stock Operator (Wiley Investment Classics)

(3) Currency Trading for Dummies, by Mark Galant and Brian Dolan A great first read as an introduction to the world of currency trading. Currency Trading For Dummies

(4) Face the Trader Within, by Chris Lori Ex-Olympian and renowned fund trader, as well as Commodities Trading Adviser and mentor to traders of all levels, Chris is uniquely positioned to write a book on the psychology of trading. Thats what this book is about: facing your inner challenges in order to achieve outer successes. An excellent read, Chris doesnt waste words and gets right to the point of what you need to know. I hate to be dogmatic but you really must read this, especially as it is now available free of charge: check the link in Free Forex Books (5) Warrior Trading, by Clifford Bennett Not as well known as your average forex book, but I list it here because I think it is worthy of a place. I personally feel that Clifford goes a little overboard with the warrior analogy, but his insight into market mechanics and market psychology is born of great experience and brilliantly expressed in this short, punchy read. Highly recommended. Warrior Trading: Inside the Mind of an Elite Currency Trader (Wiley Trading)

(6) The Secret of Candlestick Charting, by Louise Bedford The best value for money introduction to candlestick charting, and possibly the only book you need on it, with one caveat: it doesnt cover the difference between forex candlesticks and those in other markets. As I cover the topic on this site (Forex Candlesticks ) this isnt such a big deal. Highly recommendedThe Secret of Candlestick Charting: Strategies for Tading the Australian Markets

(7) Nison Candlesticks Japanese Candlestick Charting Techniques, Second Edition; Beyond Candlesticks, by Steve Nison Again Im cheating by including two books for the price of one, but anything on candlestick charting by Steve Nison is worth getting your hands on. Nison brought candlesticks to the West from Japanese culture many years ago and is still going strong, educating and updating on the subject. Again, the books have little on the differences between forex and other candlesticks, but Steve does cover forex candlesticks comprehensively in his other educational courses. Japanese Candlestick Charting Techniques, Second Edition

(8) Encyclopedia of Chart Patterns (Wiley Trading) by Thomas N. Bulkowski This really is an encyclopaedia, comprehensive and detailed explanations of chart patterns, common and uncommon. What I particularly like about Bulkowskis book is the lack of emphasis on indicators and focus on price action as discerned from the chart patterns themselves. Not an easy book to come by, but well worth the effort of a search. Encyclopedia of Chart Patterns (Wiley Trading)

(9) How to Trade a Currency Fund, by Jarratt Davis Jarratt shows the intermediate to advanced trader who has achieved consistent success how to go about a career trading for the big dogs, i. e. currency funds. This is where the big bucks are in currency trading if youre any good, and this book is an excellent read, not only for those considering such a path, but as an insight into the world of currency trading inside financial institutions for all traders, new and experienced. How to Trade a Currency Fund

(10) Day Trading and Swing Trading the Currency Market: Technical and Fundamental Strategies to Profit from Market Moves – by Kathy Lien Kathy Lien has a reputation as a currency trader and educator of value and integrity stretching across the Internet. When she speaks, you should listen. When she writes, you should read. Again, highly recommendedDay Trading and Swing Trading the Currency Market: Technical and Fundamental Strategies to Profit from Market Moves (Wiley Trading)

Some others that didnt quite make it into the list, if only for the reason that I havent read them in a while, but are also worth considering:

How I Made Two Million Dollars in the Stock Market, by Nicolas Darvas

Candlestick and Pivot Point Trading Triggers + CD-ROM: Setups for Stock, Forex, and Futures Markets by John L. Person

In this article, we introduce a new methodology to empirically identify the primary strategies used by a trader using only post-trade fill data. To do this, we apply a well-established statistical clustering technique called k - means to a sample of progress charts, representing the portion of the order completed by each point in the day as a measure of a trade’s aggressiveness. Our methodology identifies the primary strategies used by a trader and determines which strategy the trader used for each order in the sample. Having identified the strategy used for each order, trading cost analysis can be performed by strategy. We also discuss ways to exploit this technique to characterize trader behavior, assess trader performance, and suggest the appropriate benchmarks for each distinct trading strategy.

Jeff Bacidore

Jeff Bacidore is the managing director and head of Algorithmic Trading at ITG, Inc. in New York, NY. jeff. bacidoreitg

Kathryn Berkow

Kathryn Berkow is a quantitative analyst for Algorithmic Trading at ITG, Inc. in New York, NY. kathryn. berkowitg

Ben Polidore is the director of Algorithmic Trading at ITG, Inc. in New York, NY. benjamin. polidoreitg

Nigam Saraiya

Nigam Saraiya is a vice president of Algorithmic Trading at ITG, Inc. in New York, NY. nigam. saraiyaitg

In this paper we explore the specific role of randomness in financial markets, inspired by the beneficial role of noise in many physical systems and in previous applications to complex socio-economic systems. After a short introduction, we study the performance of some of the most used trading strategies in predicting the dynamics of financial markets for different international stock exchange indexes, with the goal of comparing them to the performance of a completely random strategy. In this respect, historical data for FTSE-UK, FTSE-MIB, DAX, and S P500 indexes are taken into account for a period of about 15–20 years (since their creation until today).

: 2013 Biondo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors have no support or funding to report.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In physics, both at the classical and quantum level, many real systems work fine and more efficiently due to the useful role of a random weak noise [1] –[6]. But not only physical systems benefits from disorder. In fact, noise has a great influences on the dynamics of cells, neurons and other biological entities, but also on ecological, geophysical and socio-economic systems. Following this line of research, we have recently investigated how random strategies can help to improve the efficiency of a hierarchical group in order to face the Peter principle[7] –[9] or a public institution such as a Parliament [10]. Other groups have successfully explored similar strategies in minority and Parrondo games [11]. [12]. in portfolio performance evaluation [13] and in the context of the continuous double auction [14] .

Recently Taleb has brilliantly discussed in his successful books [15]. [16] how chance and black swans rule our life, but also economy and financial market behavior beyond our personal and rational expectations or control. Actually, randomness enters in our everyday life although we hardly recognize it. Therefore, even without being skeptic as much as Taleb, one could easily claim that we often misunderstand phenomena around us and are fooled by apparent connections which are only due to fortuity. Economic systems are unavoidably affected by expectations, both present and past, since agents’ beliefs strongly influence their future dynamics. If today a very good expectation emerged about the performance of any security, everyone would try to buy it and this occurrence would imply an increase in its price. Then, tomorrow, this security would be priced higher than today, and this fact would just be the consequence of the market expectation itself. This deep dependence on expectations made financial economists try to build mechanisms to predict future assets prices. The aim of this study is precisely to check whether these mechanisms, which will be described in detail in the next sections, are more effective in predicting the market dynamics compared to a completely random strategy.

In a previous article [17]. motivated also by some intriguing experiments where a child, a chimpanzee and darts were successfully used for remunerative investments [18]. [19]. we already found some evidence in favor of random strategies for the FTSE-UK stock market. Here we will extend this investigation to other financial markets and for new trading strategies. The paper is organized as follows. Section 2 presents a brief introduction to the debate about predictability in financial markets. In Section 3 we introduce the financial time series considered in our study and perform a detrended analysis in search for possible correlations of some kind. In Section 4 we define the trading strategies used in our simulations while, in Section5, we discuss the main results obtained. Finally, in Section6, we draw our conclusions, suggesting also some counterintuitive policy implications.

Expectations and Predictability in Financial Markets

As Simon [20] pointed out, individuals assume their decision on the basis of a limited knowledge about their environment and thus face high search costs to obtain needed information. However, normally, they cannot gather all information they should. Therefore, agents act on the basis of bounded rationality . which leads to significant biases in the expected utility maximization that they pursue. In contrast, Friedman [21] defended the rational agent approach, which considers that the behavior of agents can be best described assuming their rationality, since non-rational agents do not survive competition on the market and are driven out of it. Therefore, neither systematic biases in expected utility, nor bounded rationality can be used to describe agents’ behaviors and their expectations.

Without any fear of contradiction, one could say that nowadays two main reference models of expectations have been widely established within the economics literature: the adaptive expectations model and the rational expectation model. Here we will not give any formal definition of these paradigms. For our purposes, it is sufficient to recall their rationale. The adaptive expectations model is founded on a somehow weighted series of backward-looking values (so that the expected value of a variable is the result of the combination of its past values). In contrast, the rational expectations model hypothesizes that all agents have access to all the available information and, therefore, know exactly the model that describes the economic system (the expected value of a variable is then the objective prediction provided by theory). These two theories dates back to very relevant contributions, among which we just refer to Friedman [21]. [22]. Phelps [23]. and Cagan [24] for adaptive expectations (it is however worth to notice that the notion of “adaptive expectations” has been first introduced by Arrow and Nerlove [25] ). For rational expectations we refer to Muth [26]. Lucas [27]. and Sargent-Wallace [28] .

Financial markets are often taken as example for complex dynamics and dangerous volatility. This somehow suggests the idea of unpredictability. Nonetheless, due to the relevant role of those markets in the economic system, a wide body of literature has been developed to obtain some reliable predictions. As a matter of fact, forecasting is the key point of financial markets. Since Fama [29]. we say a market is efficient if perfect arbitrage occurs. This means that the case of inefficiency implies the existence of opportunities for unexploited profits and, of course, traders would immediately operate long or short positions until any further possibility of profit disappears. Jensen [30] states precisely that a market is to be considered efficient with respect to an information set if it is impossible to make profits by trading on the basis of that given information set. This is consistent with Malkiel [31]. who argues that an efficient market perfectly reflects all information in determining assets’ prices. As the reader can easily understand, the more important part of this definition of efficiency relies on the completeness of the information set. In fact, Fama [29] distinguishes three forms of market efficiency, according to the degree of completeness of the informative set (namely “weak”, “semi-strong”, and “strong”). Thus, traders and financial analysts continuously seek to expand their information set to gain the opportunity to choose the best strategy: this process involves agents so much in price fluctuations that, at the end of the day, one could say that their activity is reduced to a systematic guess. The complete globalization of financial markets amplified this process and, eventually, we are experiencing decades of extreme variability and high volatility.

Keynes argued, many years ago, that rationality of agents and mass psychology (so-called “animal spirits”) should not be interpreted as if they were the same thing. The Author introduced the very famous beauty contest example to explain the logic underneath financial markets. In his General Theory [32] he wrote that “ investment based on genuine long-term expectations is so difficult as to be scarcely practicable. He who attempts it must surely lead much more laborious days and run greater risks than he who tries to guess better than the crowd how the crowd will behave; and, given equal intelligence, he may make more disastrous mistakes. ” In other words, in order to predict the winner of the beauty contest, one should try to interpret the jury’s preferred beauty, rather than pay attention on the ideal of objective beauty. In financial markets it is exactly the same thing. It seems impossible to forecast prices of shares without mistakes. The reason is that no investor can know in advance the opinion “of the jury”, i. e. of a widespread, heterogeneous and very substantial mass of investors that reduces any possible prediction to just a guess.

Despite considerations like these, the so-called Efficient Market Hypothesis (whose main theoretical background is the theory of rational expectations), describes the case of perfectly competitive markets and perfectly rational agents, endowed with all available information, who choose for the best strategies (since otherwise the competitive clearing mechanism would put them out of the market). There is evidence that this interpretation of a fully working perfect arbitrage mechanism is not adequate to analyze financial markets as, for example: Cutler et al. [33]. who shows that large price movements occur even when little or no new information is available; Engle [34] who reported that price volatility is strongly temporally correlated; Mandelbrot [35]. [36]. Lux [37]. Mantegna and Stanley [38] who argue that short-time fluctuations of prices are non-normal; or last but not least . Campbell and Shiller [39] who explain that prices may not accurately reflect rational valuations.

Very interestingly, a plethora of heterogeneous agents models have been introduced in the field of financial literature. In these models, different groups of traders co-exist, with different expectations, influencing each other by means of the consequences of their behaviors. Once again, our discussion cannot be exhaustive here, but we can fruitfully mention at least contributions by Brock [40]. [41]. Brock and Hommes [42]. Chiarella [43]. Chiarella and He [44]. DeGrauwe et al. [45]. Frankel and Froot [46]. Lux [47]. Wang [48]. and Zeeman [49] .

Part of this literature refers to the approach, called “adaptive belief systems”, that tries to apply non-linearity and noise to financial market models. Intrinsic uncertainty about economic fundamentals, along with errors and heterogeneity, leads to the idea that, apart from the fundamental value (i. e. the present discounted value of the expected flows of dividends), share prices fluctuate unpredictably because of phases of either optimism or pessimism according to corresponding phases of uptrend and downtrend that cause market crises. How could this sort of erratic behavior be managed in order to optimize an investment strategy? In order to explain the very different attitude adopted by agents to choose strategies when trading on financial markets, a distinction is done between fundamentalists and chartists . The former ones base their expectations about future assets’ prices upon market fundamentals and economic factors (i. e. both micro - and macroeconomic variables, such as dividends, earnings, economic growth, unemployment rates, etc). Conversely, the latter ones try to extrapolate trends or statistically relevant characteristics from past series of data, in order to predict future paths of assets prices (also known as technical analysis).

Given that the interaction of these two groups of agents determines the evolution of the market, we choose here to focus on chartists’ behavior (since a qualitative analysis on macroeconomic fundamentals is absolutely subjective and difficult to asses), trying to evaluate the individual investor’s ex-ante predictive capacity. Assuming the lack of complete information, randomness plays a key role, since efficiency is impossible to be reached. This is particularly important in order to underline that our approach does not rely on any form of the above mentioned Efficient Markets Hypothesis paradigm. More precisely, we are seeking for the answer to the following question: if a trader assumes the lack of complete information through all the market (i. e. the unpredictability of stock prices dynamics [50] –[53] ), would an ex-ante random trading strategy perform, on average, as good as well-known trading strategies? We move from the evidence that, since each agent relies on a different information set in order to build his/her trading strategies, no efficient mechanism can be invoked. Instead, a complex network of self-influencing behavior, due to asymmetric circulation of information, develops its links and generates herd behaviors to follow some signals whose credibility is accepted.

Financial crises show that financial markets are not immune to failures. Their periodic success is not free of charge . catastrophic events burn enormous values in dollars and the economic systems in severe danger. Are traders so sure that elaborated strategies fit the dynamics of the markets? Our simple simulation will perform a comparative analysis of the performance of different trading strategies: our traders will have to predict, day by day, if the market will go up (‘bullish’ trend) or down (‘bearish’ trend). Tested strategies are: the Momentum, the RSI, the UPD, the MACD, and a completely Random one.

Rational expectations theorists would immediately bet that the random strategy would loose the competition as it is not making use of any information but, as we will show, our results are quite surprising.

Detrended Analysis of the Index Time Series

We consider four very popular indexes of financial markets and in particular, we analyze the following corresponding time series, shown in Fig. 1:

Expand Figure 1. Temporal evolution of four important financial market indexes (over time intervals going from 3714 to 5750 days).

From the top to the bottom, we show the FTSE UK All-Share index, the FTSE MIB All-Share index, the DAX All-Share index and the S P 500 index. See text for further details.

In general, the possibility to predict financial time series has been stimulated by the finding of some kind of persistent behavior in some of them [38]. [54]. [55]. The main purpose of the present section is to investigate the possible presence of correlations in the previous four financial series of European and US stock market all share indexes. In this connection, we will calculate the time-dependent Hurst exponent by using the detrended moving average (DMA) technique [56]. Let us begin with a summary of the DMA algorithm. The computational procedure is based on the calculation of the standard deviation along a given time series defined as

where is the average calculated in each time window of size . In order to determine the Hurst exponent . the function is calculated for increasing values of inside the interval . being the length of the time series, and the obtained values are reported as a function of on a log-log plot. In general, exhibits a power-law dependence with exponent . i. e.

In particular, if . one has a negative correlation or anti-persistent behavior, while if one has a positive correlation or persistent behavior. The case of corresponds to an uncorrelated Brownian process. In our case, as a first step, we calculated the Hurst exponent considering the complete series. This analysis is illustrated in the four plots of Fig. 2. Here, a linear fit to the log-log plots reveals that all the values of the Hurst index H obtained in this way for the time series studied are, on average, very close to 0.5. This result seems to indicate an absence of correlations on large time scales and a consistence with a random process.

The power law behavior of the DMA standard deviation allows to derive an Hurst index that, in all the four cases, oscillates around 0.5, thus indicating an absence of correlations, on average, over large time periods. See text.

doi:10.1371/journal. pone.0068344.g002

On the other hand, it is interesting to calculate the Hurst exponent locally in time. In order to perform this analysis, we consider subsets of the complete series by means of sliding windows of size . which move along the series with time step . This means that, at each time . we calculate the inside the sliding window by changing with in Eq.(1). Hence, following the same procedure described above, a sequence of Hurst exponent values is obtained as function of time. In Fig. 3 we show the results obtained for the parameters . . In this case, the values obtained for the Hurst exponent differ very much locally from 0.5, thus indicating the presence of significant local correlations.

Expand Figure 3. Time dependence of the Hurst index for the four series: on smaller time scales, significant correlations are present.

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This investigation, which is in line with what was found previously in Ref. [56] for the Dax index, seems to suggest that correlations are important only on a local temporal scale, while they cancel out averaging over long-term periods. As we will see in the next sections, this feature will affect the performances of the trading strategies considered.

Trading Strategies Description

In the present study we consider five trading strategies defined as follows:

Random (RND) Strategy

This strategy is the simplest one, since the correspondent trader makes his/her prediction at time completely at random (with uniform distribution).

Momentum (MOM) Strategy

This strategy is based on the so called ‘momentum’ indicator, i. e. the difference between the value and the value . where is a given trading interval (in days). Then, if . the trader predicts an increment of the closing index for the next day (i. e. it predicts that ) and vice-versa. In the following simulations we will consider days, since this is one of the most used time lag for the momentum indicator. See Ref. [57] .

Relative Strength Index (RSI) Strategy

This strategy is based on a more complex indicator called ‘RSI’. It is considered a measure of the stock’s recent trading strength and its definition is: . where is the ratio between the sum of the positive returns and the sum of the negative returns occurred during the last days before . Once calculated the RSI index for all the days included in a given time-window of length immediately preceding the time . the trader which follows the RSI strategy makes his/her prediction on the basis of a possible reversal of the market trend, revealed by the so called ‘divergence’ between the original time series and the new RSI one. A divergence can be defined referring to a comparison between the original data series and the generated RSI-series, and it is the most significant trading signal delivered by any oscillator-style indicator. It is the case when the significant trend between two local extrema shown by the RSI trend is oriented in the opposite direction to the significant trend between two extrema (in the same time lag) shown by the original series. When the RSI line slopes differently from the original series line, a divergence occurs. Look at the example in Fig. 4: two local maxima follow two different trends sloped oppositely. In the case shown, the analyst will interpret this divergence as a bullish expectation (since the RSI oscillator diverges from the original series: it starts increasing when the original series is still decreasing). In our simplified model, the presence of such a divergence translates into a change in the prediction of the sign, depending on the bullish or bearish trend of the previous days. In the following simulations we will choose days, since - again - this value is one of the mostly used in RSI-based actual trading strategies. See Ref. [57] .

Up and Down Persistency (UPD) Strategy

This deterministic strategy does not come from technical analysis. However, we decided to consider it because it seems to follows the apparently simple alternate “up and down” behavior of market series that any observer can see at first sight. The strategy is based on the following very simple rule: the prediction for tomorrow market’s behavior is just the opposite of what happened the day before. If, e. g. one has . the expectation at time for the period will be bullish: . and vice versa.

Moving Average Convergence Divergence (MACD) Strategy

The ‘MACD’ is a series built by means of the difference between two Exponential Moving Averages (EMA, henceforth) of the market price, referred to two different time windows, one smaller and one larger. In any moment t . . In particular, the first is the Exponential Moving Average of taken over twelve days, whereas the second refers to twenty-six days. The calculation of these EMAs on a pre-determined time lag, x . given a proportionality weight . is executed by the following recursive formula: with . where . Once the MACD series has been calculated, its 9-days Exponential Moving Average is obtained and, finally, the trading strategy for the market dynamics prediction can be defined: the expectation for the market is bullish (bearish) if ( ). See Ref. [57] .

Expand Figure 4. RSI divergence example.

A divergence is a disagreement between the indicator (RSI) and the underlying price. By means of trend-lines, the analyst check that slopes of both series agree. When the divergence occurs, an inversion of the price dynamic is expected. In the example a bullish period is expected.

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Results of Empirically Based Simulations

For each one of our four financial time series of length (in days), the goal was simply to predict, day by day and for each strategy, the upward (bullish) or downward (bearish) movement of the index at a given day with respect to the closing value one day before: if the prediction is correct, the trader wins, otherwise he/she looses. In this connection we are only interested in evaluating the percentage of wins achieved by each strategy, assuming that - at every time step - the traders perfectly know the past history of the indexes but do not possess any other information and can neither exert any influence on the market, nor receive any information about future moves.

In the following, we test the performance of the five strategies by dividing each of the four time series into a sequence of trading windows of equal size (in days) and evaluating the average percentage of wins for each strategy inside each window while the traders move along the series day by day, from to . This procedure, when applied for . allows us to explore the performance of the various strategies for several time scales (ranging, approximatively, from months to years).

The motivation behind this choice is connected to the fact that the time evolution of each index clearly alternates between calm and volatile periods, which at a finer resolution would reveal a further, self-similar, alternation of intermittent and regular behavior over smaller time scales, a characteristic feature of turbulent financial markets [35]. [36]. [38]. [58]. Such a feature makes any long-term prediction of their behavior very difficult or even impossible with instruments of standard financial analysis. The point is that, due to the presence of correlations over small temporal scales (as confirmed by the analysis of the time dependent Hurst exponent in Fig. 3 ), one might expect that a given standard trading strategy, based on the past history of the indexes, could perform better than the others inside a given time window. But this could depend much more on chance than on the real effectiveness of the adopted algorithm. On the other hand, if on a very large temporal scale the financial market time evolution is an uncorrelated Brownian process (as indicated by the average Hurst exponent, which result to be around for all the financial time series considered), one might also expect that the performance of the standard trading strategies on a large time scale becomes comparable to random ones. In fact, this is exactly what we found as explained in the following.

In Figs. 5 –8. we report the results of our simulations for the four stock indexes considered (FTSE-UK, FTSE-MIB, DAX, S P 500). In each figure, from top to bottom, we plot: the market time series as a function of time; the correspondent ‘returns’ series, determined as the ratio ; the volatility of the returns, i. e. the variance of the previous series, calculated inside each window for increasing values of the trading window size (equal to, from left to right, . . and respectively); the average percentage of wins for the five trading strategies considered, calculated for the same four kinds of windows (the average is performed over all the windows in each configuration, considering different simulation runs inside each window); the corresponding standard deviations for the wins of the five strategies.

Expand Figure 5. Results for the FTSE-UK index series, divided into an increasing number of trading-windows of equal size (3,9,18,30), simulating different time scales.

From top to bottom, we report the index time series, the corresponding returns time series, the volatility, the percentages of wins for the five strategies over all the windows and the corresponding standard deviations. The last two quantities are averaged over 10 different runs (events) inside each window.

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More » Expand Figure 6. Results for the FTSE-MIB index series, divided into an increasing number of trading-windows of equal size (3,9,18,30), simulating different time scales.

From top to bottom, we report the index time series, the corresponding returns time series, the volatility, the percentages of wins for the five strategies over all the windows and the corresponding standard deviations. The last two quantities are averaged over 10 different runs (events) inside each window.

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More » Expand Figure 7. Results for the DAX index series, divided into an increasing number of trading-windows of equal size (3,9,18,30), simulating different time scales.

From top to bottom, we report the index time series, the corresponding returns time series, the volatility, the percentages of wins for the five strategies over all the windows and the corresponding standard deviations. The last two quantities are averaged over 10 different runs (events) inside each window.

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More » Expand Figure 8. Results for the S P 500 index series, divided into an increasing number of trading-windows of equal size (3,9,18,30), simulating different time scales.

From top to bottom, we report the index time series, the corresponding returns time series, the volatility, the percentages of wins for the five strategies over all the windows and the corresponding standard deviations. The last two quantities are averaged over 10 different runs (events) inside each window.

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Observing the last two panels in each figure, two main results are evident:

The average percentages of wins for the five strategies are always comparable and oscillate around . with small random differences which depend on the financial index considered. The performance of of wins for all the strategies may seem paradoxical, but it depends on the averaging procedure over all the windows along each time series. In Fig. 9 we show, for comparison, the behavior of the various strategies for the four financial indexes considered and for the case (the score in each window is averaged over different events): as one can see, within a given trading window each single strategy may randomly perform much better or worse than . but on average the global performance of the different strategies is very similar. Moreover, referring again to Figs. 5 –8. it is worth to notice that the strategy with the highest average percentage of wins (for most of the windows configurations) changes from one index to another one: for FTSE-UK, the MOM strategy seems to have a little advantage; for FTSE-MIB, the UPD seems to be the best one; for DAX, the RSI, and for the S P 500, the UPD performs slightly better than the others. In any case the advantage of a strategy seems purely coincidental.

The second important result is that the fluctuations of the random strategy are always smaller than those of the other strategies (as it is also visible in Fig. 9 for the case ): this means that the random strategy is less risky than the considered standard trading strategies, while the average performance is almost identical. This implies that, when attempting to optimize the performance, standard traders are fooled by the “illusion of control” phenomenon [11]. [12]. reinforced by a lucky sequence of wins in a given time window. However, the first big loss may drive them out of the market. On the other hand, the effectiveness of random strategies can be probably related to the turbulent and erratic character of the financial markets: it is true that a random trader is likely to win less in a given time window, but he/she is likely also to loose less. Therefore his/her strategy implies less risk, as he/she has a lower probability to be thrown out of the game.

Expand Figure 9. The percentage of wins of the different strategies inside each time window - averaged over 10 different events - is reported, in the case N w = 30, for the four markets considered.

As visible, the performances of the strategies can be very different one from the others inside a single time window, but averaging over the whole series these differences tend to disappear and one recovers the common outcome shown in the previous figures.

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In this paper we have explored the role of random strategies in financial systems from a micro-economic point of view. In particular, we simulated the performance of five trading strategies, including a completely random one, applied to four very popular financial markets indexes, in order to compare their predictive capacity. Our main result, which is independent of the market considered, is that standard trading strategies and their algorithms, based on the past history of the time series, although have occasionally the chance to be successful inside small temporal windows, on a large temporal scale perform on average not better than the purely random strategy, which, on the other hand, is also much less volatile. In this respect, for the individual trader, a purely random strategy represents a costless alternative to expensive professional financial consulting, being at the same time also much less risky, if compared to the other trading strategies.

This result, obtained at a micro-level, could have many implications for real markets also at the macro-level, where other important phenomena, like herding, asymmetric information, rational bubbles occur. In fact, one might expect that a widespread adoption of a random approach for financial transactions would result in a more stable market with lower volatility. In this connection, random strategies could play the role of reducing herding behavior over the whole market since, if agents knew that financial transactions do not necessarily carry an information role, bandwagon effects could probably fade. On the other hand, as recently suggested by one of us [59]. if the policy-maker (Central Banks) intervened by randomly buying and selling financial assets, two results could be simultaneously obtained. From an individual point of view, agents would suffer less for asymmetric or insider information, due to the consciousness of a “fog of uncertainty” created by the random investments. From a systemic point of view, again the herding behavior would be consequently reduced and eventual bubbles would burst when they are still small and are less dangerous; thus, the entire financial system would be less prone to the speculative behavior of credible “guru” traders, as explained also in [60]. Of course, this has to be explored in detail as well as the feedback effect of a global reaction of the market to the application of these actions. This topic is however beyond the goal of the present paper and it will be investigated in a future work.

Acknowledgments

We thank H. Trummer for DAX historical series and the other institutions for the respective data sets.

Originally posted by PeterWells

I think that you mean something where you can record each time that you enter a position and when you close it, probably put a picture and yada yada.

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In this paper we explore the specific role of randomness in financial markets, inspired by the beneficial role of noise in many physical systems and in previous applications to complex socio-economic systems. After a short introduction, we study the performance of some of the most used trading strategies in predicting the dynamics of financial markets for different international stock exchange indexes, with the goal of comparing them to the performance of a completely random strategy. In this respect, historical data for FTSE-UK, FTSE-MIB, DAX, and S P500 indexes are taken into account for a period of about 15–20 years (since their creation until today).

: 2013 Biondo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors have no support or funding to report.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In physics, both at the classical and quantum level, many real systems work fine and more efficiently due to the useful role of a random weak noise [1] –[6]. But not only physical systems benefits from disorder. In fact, noise has a great influences on the dynamics of cells, neurons and other biological entities, but also on ecological, geophysical and socio-economic systems. Following this line of research, we have recently investigated how random strategies can help to improve the efficiency of a hierarchical group in order to face the Peter principle[7] –[9] or a public institution such as a Parliament [10]. Other groups have successfully explored similar strategies in minority and Parrondo games [11]. [12]. in portfolio performance evaluation [13] and in the context of the continuous double auction [14] .

Recently Taleb has brilliantly discussed in his successful books [15]. [16] how chance and black swans rule our life, but also economy and financial market behavior beyond our personal and rational expectations or control. Actually, randomness enters in our everyday life although we hardly recognize it. Therefore, even without being skeptic as much as Taleb, one could easily claim that we often misunderstand phenomena around us and are fooled by apparent connections which are only due to fortuity. Economic systems are unavoidably affected by expectations, both present and past, since agents’ beliefs strongly influence their future dynamics. If today a very good expectation emerged about the performance of any security, everyone would try to buy it and this occurrence would imply an increase in its price. Then, tomorrow, this security would be priced higher than today, and this fact would just be the consequence of the market expectation itself. This deep dependence on expectations made financial economists try to build mechanisms to predict future assets prices. The aim of this study is precisely to check whether these mechanisms, which will be described in detail in the next sections, are more effective in predicting the market dynamics compared to a completely random strategy.

In a previous article [17]. motivated also by some intriguing experiments where a child, a chimpanzee and darts were successfully used for remunerative investments [18]. [19]. we already found some evidence in favor of random strategies for the FTSE-UK stock market. Here we will extend this investigation to other financial markets and for new trading strategies. The paper is organized as follows. Section 2 presents a brief introduction to the debate about predictability in financial markets. In Section 3 we introduce the financial time series considered in our study and perform a detrended analysis in search for possible correlations of some kind. In Section 4 we define the trading strategies used in our simulations while, in Section5, we discuss the main results obtained. Finally, in Section6, we draw our conclusions, suggesting also some counterintuitive policy implications.

Expectations and Predictability in Financial Markets

As Simon [20] pointed out, individuals assume their decision on the basis of a limited knowledge about their environment and thus face high search costs to obtain needed information. However, normally, they cannot gather all information they should. Therefore, agents act on the basis of bounded rationality . which leads to significant biases in the expected utility maximization that they pursue. In contrast, Friedman [21] defended the rational agent approach, which considers that the behavior of agents can be best described assuming their rationality, since non-rational agents do not survive competition on the market and are driven out of it. Therefore, neither systematic biases in expected utility, nor bounded rationality can be used to describe agents’ behaviors and their expectations.

Without any fear of contradiction, one could say that nowadays two main reference models of expectations have been widely established within the economics literature: the adaptive expectations model and the rational expectation model. Here we will not give any formal definition of these paradigms. For our purposes, it is sufficient to recall their rationale. The adaptive expectations model is founded on a somehow weighted series of backward-looking values (so that the expected value of a variable is the result of the combination of its past values). In contrast, the rational expectations model hypothesizes that all agents have access to all the available information and, therefore, know exactly the model that describes the economic system (the expected value of a variable is then the objective prediction provided by theory). These two theories dates back to very relevant contributions, among which we just refer to Friedman [21]. [22]. Phelps [23]. and Cagan [24] for adaptive expectations (it is however worth to notice that the notion of “adaptive expectations” has been first introduced by Arrow and Nerlove [25] ). For rational expectations we refer to Muth [26]. Lucas [27]. and Sargent-Wallace [28] .

Financial markets are often taken as example for complex dynamics and dangerous volatility. This somehow suggests the idea of unpredictability. Nonetheless, due to the relevant role of those markets in the economic system, a wide body of literature has been developed to obtain some reliable predictions. As a matter of fact, forecasting is the key point of financial markets. Since Fama [29]. we say a market is efficient if perfect arbitrage occurs. This means that the case of inefficiency implies the existence of opportunities for unexploited profits and, of course, traders would immediately operate long or short positions until any further possibility of profit disappears. Jensen [30] states precisely that a market is to be considered efficient with respect to an information set if it is impossible to make profits by trading on the basis of that given information set. This is consistent with Malkiel [31]. who argues that an efficient market perfectly reflects all information in determining assets’ prices. As the reader can easily understand, the more important part of this definition of efficiency relies on the completeness of the information set. In fact, Fama [29] distinguishes three forms of market efficiency, according to the degree of completeness of the informative set (namely “weak”, “semi-strong”, and “strong”). Thus, traders and financial analysts continuously seek to expand their information set to gain the opportunity to choose the best strategy: this process involves agents so much in price fluctuations that, at the end of the day, one could say that their activity is reduced to a systematic guess. The complete globalization of financial markets amplified this process and, eventually, we are experiencing decades of extreme variability and high volatility.

Keynes argued, many years ago, that rationality of agents and mass psychology (so-called “animal spirits”) should not be interpreted as if they were the same thing. The Author introduced the very famous beauty contest example to explain the logic underneath financial markets. In his General Theory [32] he wrote that “ investment based on genuine long-term expectations is so difficult as to be scarcely practicable. He who attempts it must surely lead much more laborious days and run greater risks than he who tries to guess better than the crowd how the crowd will behave; and, given equal intelligence, he may make more disastrous mistakes. ” In other words, in order to predict the winner of the beauty contest, one should try to interpret the jury’s preferred beauty, rather than pay attention on the ideal of objective beauty. In financial markets it is exactly the same thing. It seems impossible to forecast prices of shares without mistakes. The reason is that no investor can know in advance the opinion “of the jury”, i. e. of a widespread, heterogeneous and very substantial mass of investors that reduces any possible prediction to just a guess.

Despite considerations like these, the so-called Efficient Market Hypothesis (whose main theoretical background is the theory of rational expectations), describes the case of perfectly competitive markets and perfectly rational agents, endowed with all available information, who choose for the best strategies (since otherwise the competitive clearing mechanism would put them out of the market). There is evidence that this interpretation of a fully working perfect arbitrage mechanism is not adequate to analyze financial markets as, for example: Cutler et al. [33]. who shows that large price movements occur even when little or no new information is available; Engle [34] who reported that price volatility is strongly temporally correlated; Mandelbrot [35]. [36]. Lux [37]. Mantegna and Stanley [38] who argue that short-time fluctuations of prices are non-normal; or last but not least . Campbell and Shiller [39] who explain that prices may not accurately reflect rational valuations.

Very interestingly, a plethora of heterogeneous agents models have been introduced in the field of financial literature. In these models, different groups of traders co-exist, with different expectations, influencing each other by means of the consequences of their behaviors. Once again, our discussion cannot be exhaustive here, but we can fruitfully mention at least contributions by Brock [40]. [41]. Brock and Hommes [42]. Chiarella [43]. Chiarella and He [44]. DeGrauwe et al. [45]. Frankel and Froot [46]. Lux [47]. Wang [48]. and Zeeman [49] .

Part of this literature refers to the approach, called “adaptive belief systems”, that tries to apply non-linearity and noise to financial market models. Intrinsic uncertainty about economic fundamentals, along with errors and heterogeneity, leads to the idea that, apart from the fundamental value (i. e. the present discounted value of the expected flows of dividends), share prices fluctuate unpredictably because of phases of either optimism or pessimism according to corresponding phases of uptrend and downtrend that cause market crises. How could this sort of erratic behavior be managed in order to optimize an investment strategy? In order to explain the very different attitude adopted by agents to choose strategies when trading on financial markets, a distinction is done between fundamentalists and chartists . The former ones base their expectations about future assets’ prices upon market fundamentals and economic factors (i. e. both micro - and macroeconomic variables, such as dividends, earnings, economic growth, unemployment rates, etc). Conversely, the latter ones try to extrapolate trends or statistically relevant characteristics from past series of data, in order to predict future paths of assets prices (also known as technical analysis).

Given that the interaction of these two groups of agents determines the evolution of the market, we choose here to focus on chartists’ behavior (since a qualitative analysis on macroeconomic fundamentals is absolutely subjective and difficult to asses), trying to evaluate the individual investor’s ex-ante predictive capacity. Assuming the lack of complete information, randomness plays a key role, since efficiency is impossible to be reached. This is particularly important in order to underline that our approach does not rely on any form of the above mentioned Efficient Markets Hypothesis paradigm. More precisely, we are seeking for the answer to the following question: if a trader assumes the lack of complete information through all the market (i. e. the unpredictability of stock prices dynamics [50] –[53] ), would an ex-ante random trading strategy perform, on average, as good as well-known trading strategies? We move from the evidence that, since each agent relies on a different information set in order to build his/her trading strategies, no efficient mechanism can be invoked. Instead, a complex network of self-influencing behavior, due to asymmetric circulation of information, develops its links and generates herd behaviors to follow some signals whose credibility is accepted.

Financial crises show that financial markets are not immune to failures. Their periodic success is not free of charge . catastrophic events burn enormous values in dollars and the economic systems in severe danger. Are traders so sure that elaborated strategies fit the dynamics of the markets? Our simple simulation will perform a comparative analysis of the performance of different trading strategies: our traders will have to predict, day by day, if the market will go up (‘bullish’ trend) or down (‘bearish’ trend). Tested strategies are: the Momentum, the RSI, the UPD, the MACD, and a completely Random one.

Rational expectations theorists would immediately bet that the random strategy would loose the competition as it is not making use of any information but, as we will show, our results are quite surprising.

Detrended Analysis of the Index Time Series

We consider four very popular indexes of financial markets and in particular, we analyze the following corresponding time series, shown in Fig. 1:

Expand Figure 1. Temporal evolution of four important financial market indexes (over time intervals going from 3714 to 5750 days).

From the top to the bottom, we show the FTSE UK All-Share index, the FTSE MIB All-Share index, the DAX All-Share index and the S P 500 index. See text for further details.

In general, the possibility to predict financial time series has been stimulated by the finding of some kind of persistent behavior in some of them [38]. [54]. [55]. The main purpose of the present section is to investigate the possible presence of correlations in the previous four financial series of European and US stock market all share indexes. In this connection, we will calculate the time-dependent Hurst exponent by using the detrended moving average (DMA) technique [56]. Let us begin with a summary of the DMA algorithm. The computational procedure is based on the calculation of the standard deviation along a given time series defined as

where is the average calculated in each time window of size . In order to determine the Hurst exponent . the function is calculated for increasing values of inside the interval . being the length of the time series, and the obtained values are reported as a function of on a log-log plot. In general, exhibits a power-law dependence with exponent . i. e.

In particular, if . one has a negative correlation or anti-persistent behavior, while if one has a positive correlation or persistent behavior. The case of corresponds to an uncorrelated Brownian process. In our case, as a first step, we calculated the Hurst exponent considering the complete series. This analysis is illustrated in the four plots of Fig. 2. Here, a linear fit to the log-log plots reveals that all the values of the Hurst index H obtained in this way for the time series studied are, on average, very close to 0.5. This result seems to indicate an absence of correlations on large time scales and a consistence with a random process.

The power law behavior of the DMA standard deviation allows to derive an Hurst index that, in all the four cases, oscillates around 0.5, thus indicating an absence of correlations, on average, over large time periods. See text.

doi:10.1371/journal. pone.0068344.g002

On the other hand, it is interesting to calculate the Hurst exponent locally in time. In order to perform this analysis, we consider subsets of the complete series by means of sliding windows of size . which move along the series with time step . This means that, at each time . we calculate the inside the sliding window by changing with in Eq.(1). Hence, following the same procedure described above, a sequence of Hurst exponent values is obtained as function of time. In Fig. 3 we show the results obtained for the parameters . . In this case, the values obtained for the Hurst exponent differ very much locally from 0.5, thus indicating the presence of significant local correlations.

Expand Figure 3. Time dependence of the Hurst index for the four series: on smaller time scales, significant correlations are present.

doi:10.1371/journal. pone.0068344.g003

This investigation, which is in line with what was found previously in Ref. [56] for the Dax index, seems to suggest that correlations are important only on a local temporal scale, while they cancel out averaging over long-term periods. As we will see in the next sections, this feature will affect the performances of the trading strategies considered.

Trading Strategies Description

In the present study we consider five trading strategies defined as follows:

Random (RND) Strategy

This strategy is the simplest one, since the correspondent trader makes his/her prediction at time completely at random (with uniform distribution).

Momentum (MOM) Strategy

This strategy is based on the so called ‘momentum’ indicator, i. e. the difference between the value and the value . where is a given trading interval (in days). Then, if . the trader predicts an increment of the closing index for the next day (i. e. it predicts that ) and vice-versa. In the following simulations we will consider days, since this is one of the most used time lag for the momentum indicator. See Ref. [57] .

Relative Strength Index (RSI) Strategy

This strategy is based on a more complex indicator called ‘RSI’. It is considered a measure of the stock’s recent trading strength and its definition is: . where is the ratio between the sum of the positive returns and the sum of the negative returns occurred during the last days before . Once calculated the RSI index for all the days included in a given time-window of length immediately preceding the time . the trader which follows the RSI strategy makes his/her prediction on the basis of a possible reversal of the market trend, revealed by the so called ‘divergence’ between the original time series and the new RSI one. A divergence can be defined referring to a comparison between the original data series and the generated RSI-series, and it is the most significant trading signal delivered by any oscillator-style indicator. It is the case when the significant trend between two local extrema shown by the RSI trend is oriented in the opposite direction to the significant trend between two extrema (in the same time lag) shown by the original series. When the RSI line slopes differently from the original series line, a divergence occurs. Look at the example in Fig. 4: two local maxima follow two different trends sloped oppositely. In the case shown, the analyst will interpret this divergence as a bullish expectation (since the RSI oscillator diverges from the original series: it starts increasing when the original series is still decreasing). In our simplified model, the presence of such a divergence translates into a change in the prediction of the sign, depending on the bullish or bearish trend of the previous days. In the following simulations we will choose days, since - again - this value is one of the mostly used in RSI-based actual trading strategies. See Ref. [57] .

Up and Down Persistency (UPD) Strategy

This deterministic strategy does not come from technical analysis. However, we decided to consider it because it seems to follows the apparently simple alternate “up and down” behavior of market series that any observer can see at first sight. The strategy is based on the following very simple rule: the prediction for tomorrow market’s behavior is just the opposite of what happened the day before. If, e. g. one has . the expectation at time for the period will be bullish: . and vice versa.

Moving Average Convergence Divergence (MACD) Strategy

The ‘MACD’ is a series built by means of the difference between two Exponential Moving Averages (EMA, henceforth) of the market price, referred to two different time windows, one smaller and one larger. In any moment t . . In particular, the first is the Exponential Moving Average of taken over twelve days, whereas the second refers to twenty-six days. The calculation of these EMAs on a pre-determined time lag, x . given a proportionality weight . is executed by the following recursive formula: with . where . Once the MACD series has been calculated, its 9-days Exponential Moving Average is obtained and, finally, the trading strategy for the market dynamics prediction can be defined: the expectation for the market is bullish (bearish) if ( ). See Ref. [57] .

Expand Figure 4. RSI divergence example.

A divergence is a disagreement between the indicator (RSI) and the underlying price. By means of trend-lines, the analyst check that slopes of both series agree. When the divergence occurs, an inversion of the price dynamic is expected. In the example a bullish period is expected.

doi:10.1371/journal. pone.0068344.g004

Results of Empirically Based Simulations

For each one of our four financial time series of length (in days), the goal was simply to predict, day by day and for each strategy, the upward (bullish) or downward (bearish) movement of the index at a given day with respect to the closing value one day before: if the prediction is correct, the trader wins, otherwise he/she looses. In this connection we are only interested in evaluating the percentage of wins achieved by each strategy, assuming that - at every time step - the traders perfectly know the past history of the indexes but do not possess any other information and can neither exert any influence on the market, nor receive any information about future moves.

In the following, we test the performance of the five strategies by dividing each of the four time series into a sequence of trading windows of equal size (in days) and evaluating the average percentage of wins for each strategy inside each window while the traders move along the series day by day, from to . This procedure, when applied for . allows us to explore the performance of the various strategies for several time scales (ranging, approximatively, from months to years).

The motivation behind this choice is connected to the fact that the time evolution of each index clearly alternates between calm and volatile periods, which at a finer resolution would reveal a further, self-similar, alternation of intermittent and regular behavior over smaller time scales, a characteristic feature of turbulent financial markets [35]. [36]. [38]. [58]. Such a feature makes any long-term prediction of their behavior very difficult or even impossible with instruments of standard financial analysis. The point is that, due to the presence of correlations over small temporal scales (as confirmed by the analysis of the time dependent Hurst exponent in Fig. 3 ), one might expect that a given standard trading strategy, based on the past history of the indexes, could perform better than the others inside a given time window. But this could depend much more on chance than on the real effectiveness of the adopted algorithm. On the other hand, if on a very large temporal scale the financial market time evolution is an uncorrelated Brownian process (as indicated by the average Hurst exponent, which result to be around for all the financial time series considered), one might also expect that the performance of the standard trading strategies on a large time scale becomes comparable to random ones. In fact, this is exactly what we found as explained in the following.

In Figs. 5 –8. we report the results of our simulations for the four stock indexes considered (FTSE-UK, FTSE-MIB, DAX, S P 500). In each figure, from top to bottom, we plot: the market time series as a function of time; the correspondent ‘returns’ series, determined as the ratio ; the volatility of the returns, i. e. the variance of the previous series, calculated inside each window for increasing values of the trading window size (equal to, from left to right, . . and respectively); the average percentage of wins for the five trading strategies considered, calculated for the same four kinds of windows (the average is performed over all the windows in each configuration, considering different simulation runs inside each window); the corresponding standard deviations for the wins of the five strategies.

Expand Figure 5. Results for the FTSE-UK index series, divided into an increasing number of trading-windows of equal size (3,9,18,30), simulating different time scales.

From top to bottom, we report the index time series, the corresponding returns time series, the volatility, the percentages of wins for the five strategies over all the windows and the corresponding standard deviations. The last two quantities are averaged over 10 different runs (events) inside each window.

doi:10.1371/journal. pone.0068344.g005

More » Expand Figure 6. Results for the FTSE-MIB index series, divided into an increasing number of trading-windows of equal size (3,9,18,30), simulating different time scales.

From top to bottom, we report the index time series, the corresponding returns time series, the volatility, the percentages of wins for the five strategies over all the windows and the corresponding standard deviations. The last two quantities are averaged over 10 different runs (events) inside each window.

doi:10.1371/journal. pone.0068344.g006

More » Expand Figure 7. Results for the DAX index series, divided into an increasing number of trading-windows of equal size (3,9,18,30), simulating different time scales.

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While successful trading requires tremendous skill and knowledge, it begins and ends with mindset. What do exceptional traders think when they purchase a quality stock and the price immediately plummets? How do they keep one bad trade from destroying their confidence--and bankroll? What do they know that the rest of us don't?

"Some trades are not worth the risk and should never be done."

High Probability Trading shows you how to trade only when the odds are in your favor. From descriptions of the software and equipment an exceptional trader needs to high probability signals that either a top or bottom has been reached, it is today's most complete guidebook to thinking like an exceptional trader--every day, on every trade.

"It's not how good you are at one individual thing, but it's the culmination of every aspect of trading that makes one successful."

Before he became a successful trader, Marcel Link spent years wading from one system to the next, using trial and error to figure out what worked, what didn't, and why. In High Probability Trading . Link reveals the steps he took to become a consistent, patient, and winning trader--by learning what to watch for, what to watch out for, and what to do to make each trade a high probability trade.

"Why do a select few traders repeatedly make money while the masses lose? What do bad traders do that good traders avoid, and what do winning traders do that is different? Throughout this book I will detail how successful traders behave differently and consistently make money by making high probability trades and avoiding common pitfalls. "--From the preface

Within 6 months of beginning their careers full of promise and hope, most traders are literally out of money and out of trading. High Probability Trading reduces the likelihood that you will have to pay this "traders' tuition," by detailing a market-proven program for weathering those first few months and becoming a profitable trader from the beginning.

Combining a uniquely blunt look at the realities of trading with examples, charts, and case studies detailing actual hits and misses of both short - and long-term traders, this straightforward guidebook discusses:

The 10 consistent attributes of a successful trader, and how to make them work for you

Strategies for controlling emotions in the heat of trading battle

Technical analysis methods for identifying trends, breakouts, reversals, and more

Market-tested signals for consistently improving the timing of entry and exit points

How to "trade the news"--and understand when the market has already discounted it

Learning how to get out of a bad trade before it can hurt you

The best traders enter the markets only when the odds are in their favor. High Probability Trading shows you how to know the difference between low and high probability situations, and only trade the latter. It goes far beyond simply pointing out the weaknesses and blind spots that hinder most traders to explaining how those defects can be understood, overcome, and turned to each trader's advantage.

While it is a cliché, it is also true that there are no bad traders, only bad trades. Let High Probability Trading show you how to weed the bad trades from your trading day by helping you see them before they occur. Packed with charts, trading tips, and questions traders should be asking themselves, plus real examples of traders in every market situation, this powerful book will first give you the knowledge and tools you need to tame the markets and then show you how to meld them seamlessly into a customized trading program--one that will help you join the ranks of elite traders and increase your probability of success on every trade.

About the Author

Marcel Link has been trading professionally since 1991. He is the founder of linkfutures and is a TradeStation consultant. Linkfutures serves the trading community with daily commentary on the markets, along with insights into technical analysis, training, marketing, and other information that can be vital to traders. Link can be reached for questions or comments at marcellinkfutures.

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Chen, Dong and Gao, Yanmin and Kaul, Mayank and Leung, Charles Ka Yui and Tsang, Desmond (2014): The role of sponsor and external management on the capital structure of Asian-Pacific REITs: the case of Australia, Japan, and Singapore.

Commendatore, Pasquale and Michetti, Elisabetta and Purificato, Francesco (2013): Financial Development and Agglomeration.

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Santillan Salgado, Roberto and Hibert Sanchez, Abel (2009): A dominant firm’s strategy and its effect on the capital structure of non?dominant firms in the self?service discount stores industry. Published in: Memories of the Emerging Challenges in the Western Hemisphere Conference

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Su, EnDer (2014): Measuring Contagion Risk in High Volatility State between Major Banks in Taiwan by Threshold Copula GARCH Model.